We compare GANformer2 to both unconditional and conditional generative models. First, we inspect unconditional methods which synthesize images from scratch, including in particular: (1) a baseline GAN [1]}, (2) the StyleGAN2 [2]} model, (3) SAGAN [3]} which utilizes self-attention across spatial regions, (4) k-GAN [4]} that blends together \(k\)  generated images through alpha-composition, (5) VQGAN [5]}, a visual autoregessive autoencoder, (6) SBGAN [6]}, a non-compositional two-stage approach, and (7) the original GANformer [7]}.
